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Authors: Enrico G. Caldarola 1 ; Antonio Picariello 2 ; Antonio M. Rinaldi 3 and Marco Sacco 4

Affiliations: 1 University of Naples Federico II and National Research Council, Italy ; 2 University of Napoli Federico II, Italy ; 3 University of Naples Federico II, Italy ; 4 National Research Council, Italy

Keyword(s): Graph Database, Big Data, NoSQL, Data Visualization, DBpedia, Neo4J.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Analytics ; Data Analytics ; Data Engineering ; Information Extraction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Symbolic Systems ; Visual Data Mining and Data Visualization

Abstract: Increasingly, the data and information visualization is becoming strategic for the exploration and explanation of large data sets. The Big Data paradigm pushes for new ways, new technological solutions to deal with the big volume and the big variety of data today. Not surprisingly, a plethora of new tools have emerged, each of them with pros and cons, but all espousing the cause of "Bigness of Data". In this paper, we take one of this emerging tools, namely Neo4J, and stress its capabilities in order to import, query and visualize data coming from a \emph{big} case study: DBpedia. We will describe each step in this study focusing on the used strategies for overcoming the different problems mainly due to the intricate nature of the case study and its volume. We confront with both the intensional schema of DBpedia and its extensional part in order to obtain the best result in its visualization. Finally, an attempt to define some criteria to simplify the large-scale visualization of DBp edia will be made, providing some examples and considerations which have arisen. The ultimate goal of this work is to investigate techniques and approaches to get more insights from the visual representation and analytics of large graph databases. (More)

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Paper citation in several formats:
G. Caldarola, E.; Picariello, A.; M. Rinaldi, A. and Sacco, M. (2016). Exploration and Visualization of Big Graphs - The DBpedia Case Study. In Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2016) - KDIR; ISBN 978-989-758-203-5; ISSN 2184-3228, SciTePress, pages 257-264. DOI: 10.5220/0006046802570264

@conference{kdir16,
author={Enrico {G. Caldarola}. and Antonio Picariello. and Antonio {M. Rinaldi}. and Marco Sacco.},
title={Exploration and Visualization of Big Graphs - The DBpedia Case Study},
booktitle={Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2016) - KDIR},
year={2016},
pages={257-264},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006046802570264},
isbn={978-989-758-203-5},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2016) - KDIR
TI - Exploration and Visualization of Big Graphs - The DBpedia Case Study
SN - 978-989-758-203-5
IS - 2184-3228
AU - G. Caldarola, E.
AU - Picariello, A.
AU - M. Rinaldi, A.
AU - Sacco, M.
PY - 2016
SP - 257
EP - 264
DO - 10.5220/0006046802570264
PB - SciTePress